Letterboxd Network Analysis

Welcome to my Letterboxd Network Analysis repository! This repository contains the code, data, and analyses used in my exploration of social networks on the Letterboxd platform. The work is split across three papers, each focusing on different aspects of network theory, information diffusion, and link prediction. Below, you’ll find a guide to the contents of this repository and how to get started. (I'm sorry things arent well organized, I'll work on it!)

Repository Structure

📂 fav_genres/

• Description: This folder contains data and examples of how movie genre preferences were analyzed.
• Contents:
  • genre_data_1.json - Sample scraped data of user genre preferences.
  • genre_data_2.json - Another sample of genre preferences data.
  • genre_data_3.json - Additional example data for genre analysis.
  • genre_analysis.ipynb - Jupyter Notebook demonstrating the analysis of genre preferences and how to use the scraped data.
  • genre_utils.py - Python script containing the functions used in the genre analysis.

📂 lbxd_network/

• Description: Contains the materials used for network visualization and further analysis of Letterboxd social connections.

• Contents:

  • network_visualization_1.html - Interactive HTML file for visualizing the Letterboxd network.
  • network_visualization_2.html - Another interactive visualization of a different aspect of the network.
  • network_visualization_3.html - Additional network visualization to explore.
  • utils.py - Utility functions used across different network analyses.
  • network_analysis_1.ipynb - Jupyter Notebook showcasing various visualizations and tools.
  • network_analysis_2.ipynb - Further network exploration and tool usage.
  • network_analysis_3.ipynb - Additional insights and visualizations related to network theory and link prediction.

  Note: The contents of this folder are somewhat disorganized, but improvements will be made in future updates.

Usage

1. Clone the repository:

   git clone https://github.com/your-username/Letterboxd-Network-Analysis.git

2. Navigate to the appropriate folder:

• Explore genre preferences in fav_genres/
• Investigate network visualizations in lbxd_network/

3. Run the Jupyter Notebooks: Use the notebooks to understand how the data was processed and visualized. The Python scripts contain all the necessary functions for running the analyses.

License

This project is licensed under the MIT License.

Contributing

Contributions are welcome! Feel free to fork the repository and submit pull requests. If you have any suggestions or find any issues, please open an issue.

Acknowledgements

Thanks to the Letterboxd community and the open-source community for the tools and resources that made this analysis possible.